Satellite Constellation Integrated Optimization Framework
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Solution Overview
Problem
Current optimization methods for satellite constellations focus on independent subsystem optimization, leading to increased complexity, higher design costs, and suboptimal performance due to neglect of interdependencies between subsystems, resulting in inefficient manufacturing and launch processes.
Innovation Solution
An integrated optimization framework that defines relationships between all subsystems, using automated design coordinators and cause-effect engines to optimize each subsystem individually and collectively, while also considering system-level priorities, and employing image-based coverage calculations and power optimization techniques to reduce the number of satellites required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If independent subsystem optimization is performed, then optimization process is simpler, but overall system optimality is lost and complexity increases
Solution Approach 1:
The system divides the satellite constellation into multiple independent subsystems (payload, platform, propulsion, power, etc.), each optimized separately through automated tools. This segmentation allows simpler individual optimizations while maintaining overall system coherence through the integrated framework that manages inter-subsystem dependencies.
Solution Approach 2:
An integrated optimization framework acts as an intermediary between independent subsystem optimizations. This framework coordinates the various subsystem optimizations, manages their interdependencies, and ensures that individual optimizations contribute to overall system optimality rather than creating conflicts.
2Device complexity
If subsystem optimizations are performed independently, then design process is simpler, but manufacturing and launch costs increase
Solution Approach 1:
The design process segments the satellite constellation into independent subsystems that can be optimized separately using automated tools. This reduces design process complexity while the integrated framework ensures cost-effective outcomes by coordinating these segmented optimizations to avoid redundant manufacturing and launch requirements.
Solution Approach 2:
The system automatically adjusts design parameters of subsystems based on optimization criteria, enabling cost-effective configurations. By systematically varying and optimizing parameters across subsystems within the integrated framework, the solution achieves lower manufacturing and launch costs while maintaining design simplicity.
3Device complexity
If focus is limited to subsystem level optimization, then optimization is more manageable, but performance of satellite constellation is reduced
Solution Approach 1:
The approach segments the optimization problem into manageable subsystem levels while using an integrated framework to coordinate them. This segmentation makes optimization manageable, while the framework ensures that subsystem optimizations collectively enhance overall constellation performance through proper coordination of interdependencies.
Solution Approach 2:
The integrated optimization framework serves multiple functions simultaneously: it manages individual subsystem optimizations, coordinates inter-subsystem dependencies, ensures system-wide optimality, and evaluates overall constellation performance. This multi-functionality allows manageable subsystem-level work to contribute to enhanced overall performance.
4Reliability
If handling inter-dependencies is attempted, then overall system optimality improves, but complexity and design costs increase
Solution Approach 1:
The integrated optimization framework acts as an intermediary that manages inter-dependencies between subsystems. It coordinates the optimizations across subsystems, ensuring that inter-dependencies are properly handled to achieve overall system optimality while preventing exponential complexity growth through systematic management approaches.
Solution Approach 2:
The system automatically adjusts and optimizes parameters across subsystems by considering their inter-dependencies. Through systematic parameter changes and coordination within the integrated framework, the solution achieves overall system optimality while managing complexity through automated optimization algorithms rather than manual analysis.
Data Source
AI summary
The embodiments herein provide a system and method for integrated optimization of design and performance of satellite constellations. The present disclosure provides a method for optimization of design and performance of satellite constellation to provide internet connectivity at preset geographic regions. In current methods, the optimizations of subsystems are performed independently and the results are combined, resulting in a loss of overall optimality. The present disclosure defines the relationships between subsystems such that integrity of complete design is tested with fewer complexities and provides an integrated optimization framework, in which every subsystem is optimized individually and collectively. The present disclosure provides a method for optimization of power subsystem of satellites by determining the pattern of payload operation and need for peak power. The present disclosure also provides a method to minimize the number of satellites required in constellations by carefully regulating spot beams formed by individual satellites in constellations.